This fourth article in RBC Investor & Treasury Services' Fintech and Regulators series explores how the increasing complexity of anti-money laundering (AML) regulation is prompting firms and authorities to invest in fintech and regtech solutions.
The last several years have seen a rapid rise in the scope and complexity of AML regulations. For example, in the European Union (EU), the fourth AML directive, passed in 2015, had a transposition deadline of June 26, 2017, and the revised Wire Transfer Regulation came into effect on the same date. Fraud, money laundering and the financing of terrorist organizations are a major concern for authorities internationally and will continue to be the subject of future legislation.
Key insights
- Technology companies like IBM are exploring ways of improving KYC procedures using blockchain technology
- The Financial Conduct Authority in the UK is piloting the use of the machine learning algorithms to identify fraudulent activities within financial data
- Client due diligence procedures vary from firm to firm and should reflect the size and complexity of the organization
According to a PwC Global Economic Crime Survey, global money laundering transactions are estimated to be worth between USD 1-2 trillion, or between 2-5 percent of global GDP.1 Despite widespread recognition of the importance of improving AML measures, effectively implementing compliance procedures remains a key challenge for many financial services firms. The PwC survey also noted that 19 percent of financial services firms claim that the ability to hire experienced staff is the biggest challenge to AML compliance.2
Financial services firms are increasingly turning to regtech and fintech vendors for digital tools to help automate compliance obligations. In particular, there is growing potential for technology-enabled innovations to optimize the process of onboarding clients, to streamline the fulfilment of know-your-customer (KYC) obligations, and to improve the reliability of due diligence and risk management.
Machine learning about your customers
One of the most pressing challenges for market participants lies in the screening of client names at the onboarding stage. With varied demographics, geographies and client risk profiles, financial institutions must rely on different tools to perform the screening checks. Failure to include all the relevant details in screening, such as names of all related or connected parties, alias names and fields in payment messages is another challenge presented by existing systems.
In addition, the manual nature of many of the AML control processes, such as KYC and transaction monitoring, often involve intensive manpower and can add significant time to client onboarding processes.3
One of the most pressing challenges for market participants lies in the screening of client names at the onboarding stage
Systems for transaction monitoring and detecting suspicious transactions are also manual in nature and can result in a large number of false positives (when a monitoring system identifies a low-value alert). Adopting artificial intelligence (AI) may be the key to increasing accuracy and reducing the number of incorrectly flagged transactions.
Some organizations have begun to use AI for AML activities, particularly for transaction monitoring and surveillance purposes. In the UK, the Financial Conduct Authority is experimenting with machine learning processes that mine social media content to improve the accuracy of KYC checks.4
Blockchain presents another opportunity. In Singapore, a fintech startup named KYCK! is working with IBM to modernize the way client onboarding is managed using blockchain technology. The platform provides enhanced identification verification and enables automatic bank-checks with trusted third parties.5
Utilizing Big Data for risk management
AML due diligence requirements could clearly benefit from process innovation. Financial institutions are typically accessing different sources for information about their clients. Existing processes to identify the appropriate risk categories for clients remains largely manual.
AML due diligence requirements could clearly benefit from process innovation
The use of disparate technology solutions that do not have the ability to inter-operate within the same financial institution, may result in client information being inadequately captured for assessment. Inappropriate management of that data could result in operational risk and prevent organizations from accurately understanding client risks, performing name screening and monitoring transactions.
Fintech and regtech providers are using Big Data analytics, cloud computing and machine learning to support them in offering AML compliance solutions, including:
- automation of risk management processes
- facilitating regulatory reporting
- prevention of fraud
- enabling companies to stay abreast of regulatory changes around the world
- support for strategic planning
Regulators believe that more comprehensive guidelines and more stringent checks and balances are required to combat the global threat of fraudulent activity. However, with each new AML measure, the compliance burden on financial institutions grows. To prevent operational inefficiencies and remain agile in an environment where fintech and regtech are growing, firms must embrace emerging technologies that can assist in reducing the cumbersome processes that come with their compliance obligations.
July 28, 2017
Fintech and stability
August 31, 2017
Fintech and regulators II: Assessing the implications
Sources
- PwC (February 24, 2016) Global Economic Crime Survey 2016
- Ibid. PwC
- Bovill (July 27, 2017) AML challenges: From client onboarding to ongoing monitoring
- CNBC (July 13, 2017) UK regulator looking to use A.I., machine-learning to enforce financial compliance
- IBM Singapore (March 17, 2017) IBM Blockchain Helps FinTechs and Banks Address KYC Challenge